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Signal alignment has become a popular problem in robotics due in part to itsfundamental role in action recognition. Currently, the most successfulalgorithms for signal alignment are Dynamic Time Warping (DTW) and its variant'Fast' Dynamic Time Warping (FastDTW). Here we introduce a new framework forsignal alignment, namely the Globally Optimal Reparameterization Algorithm(GORA). We review the algorithm's mathematical foundation and provide anumerical verification of its theoretical basis. We compare the performance ofGORA with that of the DTW and FastDTW algorithms, in terms of computationalefficiency and accuracy in matching signals. Our results show a significantimprovement in both speed and accuracy over the DTW and FastDTW algorithms andsuggest that GORA has the potential to provide a highly effective framework forsignal alignment and action recognition.

The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences